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vit-base-HAM-10000-sharpened-large-patch-16
This model is a fine-tuned version of google/vit-large-patch16-224-in21k on the ahishamm/HAM_db_sharpened dataset. It achieves the following results on the evaluation set:
- Loss: 0.5504
- Accuracy: 0.8075
- Recall: 0.8075
- F1: 0.8075
- Precision: 0.8075
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision |
---|---|---|---|---|---|---|---|
0.9294 | 0.2 | 100 | 1.0377 | 0.6733 | 0.6733 | 0.6733 | 0.6733 |
1.0067 | 0.4 | 200 | 0.8976 | 0.6813 | 0.6813 | 0.6813 | 0.6813 |
1.0081 | 0.6 | 300 | 0.9345 | 0.6773 | 0.6773 | 0.6773 | 0.6773 |
0.9326 | 0.8 | 400 | 0.8494 | 0.6883 | 0.6883 | 0.6883 | 0.6883 |
0.8243 | 1.0 | 500 | 0.7481 | 0.7267 | 0.7267 | 0.7267 | 0.7267 |
0.7408 | 1.2 | 600 | 0.7277 | 0.7317 | 0.7317 | 0.7317 | 0.7317 |
0.6844 | 1.4 | 700 | 0.7114 | 0.7392 | 0.7392 | 0.7392 | 0.7392 |
0.7411 | 1.6 | 800 | 0.6772 | 0.7416 | 0.7416 | 0.7416 | 0.7416 |
0.7138 | 1.8 | 900 | 0.7136 | 0.7377 | 0.7377 | 0.7377 | 0.7377 |
0.5838 | 2.0 | 1000 | 0.6625 | 0.7521 | 0.7521 | 0.7521 | 0.7521 |
0.5315 | 2.2 | 1100 | 0.6104 | 0.7776 | 0.7776 | 0.7776 | 0.7776 |
0.6391 | 2.4 | 1200 | 0.6317 | 0.7591 | 0.7591 | 0.7591 | 0.7591 |
0.6903 | 2.59 | 1300 | 0.6098 | 0.7656 | 0.7656 | 0.7656 | 0.7656 |
0.5798 | 2.79 | 1400 | 0.6211 | 0.7751 | 0.7751 | 0.7751 | 0.7751 |
0.5448 | 2.99 | 1500 | 0.5824 | 0.7820 | 0.7820 | 0.7820 | 0.7820 |
0.4523 | 3.19 | 1600 | 0.5951 | 0.7776 | 0.7776 | 0.7776 | 0.7776 |
0.4485 | 3.39 | 1700 | 0.6114 | 0.7815 | 0.7815 | 0.7815 | 0.7815 |
0.487 | 3.59 | 1800 | 0.5730 | 0.7950 | 0.7950 | 0.7950 | 0.7950 |
0.4104 | 3.79 | 1900 | 0.5597 | 0.7965 | 0.7965 | 0.7965 | 0.7965 |
0.4468 | 3.99 | 2000 | 0.5504 | 0.8075 | 0.8075 | 0.8075 | 0.8075 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3